Video course: building reasoning models in Python and PyTorch
In brief
- 11-hour video course to implement reasoning models in Python and PyTorch using a pre-trained Qwen3 0.6B LLM.
- Covers evaluation, inference-time scaling, reinforcement learning, and distillation with concrete examples on MATH-500.
- Designed for experienced Python and PyTorch developers, with hardware recommendations for training steps.
Sebastian Raschka unveils an 11-hour video course to implement reasoning models in Python and PyTorch. According to the author, these videos accompany his book *Build a Reasoning Model (From Scratch)* and cover evaluation, inference-time scaling, reinforcement learning, and distillation.
The videos walk through building a reasoning model from a pre-trained Qwen3 0.6B LLM. They include evaluating performance on MATH-500, adding inference-scaling techniques like self-consistency voting, and integrating a math answer verifier to guide reinforcement learning.
The course targets developers comfortable with Python and PyTorch, with hardware recommendations (CPU for early steps, GPU for training). It also contrasts conventional LLMs, reasoning models, and automated agents, highlighting reasoning models as the backbone of modern agent systems.
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